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Sreedevi Sampath, University of Delaware Amie Souter, Drexel University

Towards Defining and Exploiting Similarities in Web Application Use Cases through User Session Analysis. Sreedevi Sampath, University of Delaware Amie Souter, Drexel University Lori Pollock, University of Delaware. Workshop on Dynamic Analysis (WODA), May 25, 2004 co-located with

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Sreedevi Sampath, University of Delaware Amie Souter, Drexel University

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  1. Towards Defining and Exploiting Similarities in Web Application Use Cases through User Session Analysis Sreedevi Sampath, University of Delaware Amie Souter, Drexel University Lori Pollock, University of Delaware Workshop on Dynamic Analysis (WODA), May 25, 2004 co-located with International Conference on Software Engineering (ICSE 2004)

  2. Test case generation Monitoring load of traffic Content personalization Motivation and Overview • 12.3.40.65 GET index.jsp • 12.3.40.65 GET login.jsp • 12.3.40.65 GET reg.jsp? login=xxx&password=hello& • 12.3.40.65 GET myinfo.jsp Behaviorally related sequence of events performed by the user through interaction with the system View user sessions as use cases Learn about dynamic behavior User session analysis • Clustering via concept analysis • Common subsequences analysis Software development/ maintenance tools Test suite reduction

  3. Step 1Clustering via Concept Analysis • Mathematical technique for clustering objects that have common discrete attributes • Set of objects, O: user sessions, us • Set of attributes, A: URLs, u • Relation, R: usrequestsu • Concept analysis identifies all concepts (Oi, Aj) for a given tuple (O, A, R)

  4. GD GR GL ({us1, us2, us3, us4, us5, us6} ,{GD,GR,GL}) ({us2, us3, us4, us6} ,{GD,GR,GL,GS}) ,{GD,GR,GL,PL,GS}) ({us4} ({us3} ,{GD,GR,GL,GS,GB}) ({us2} ,{GD,GR,GL,GS,GM}) ({us6} ,{GD,GR,GL,PL,GS,GB}) CONCEPT LATTICE: FULL (null, {GD,GR,GL,PL,GS,GB,GM}) attributes (URLs) RELATION TABLE us1 us5 o b j e c t s GS x x x x x us2 GB PL GM us4 us3 us2 us6 SPARSE

  5. Step 2Heuristic for Test Suite Reduction • Smallest set of user sessions • Covers all the URLs • Represents common URL subsequences of different use cases GD GR GL us1 us5 GS Identify next-to-bottom nodes GB Pick one user session from each of these next-to-bottom nodes PL GM us4 us3 Resulting reduced test suite: {us2, us6} us2 us6

  6. Hypothesis Motivating the Approach • Common Subsequences Hypothesis: The set of user sessions clustered together into the same concept node will have a high commonality in the subsequences of URLs in their sessions

  7. Finding Common Subsequences of URLs Subsequences of URLs are representative of partial use cases of the user sessions us3 us6 Common Subsequences GD GD GR GR [GD, GR, GL] [PL, GS] GL GL NODE 003 [GR, GL] PL GB objects GS PL { us3, us6 } PL GS attributes GR GR { GD, GL, GR, GS, PL } GL GL PL GB GS PL GS

  8. attr-size[5]: level 5 # user sessions= 4 # user sessions = 3 # URLs = 5 # URLs = 5 us2 us1 us3 {a, b, c, d, e} a b c d a b e a a b c e a b e d a b c d a b e Metric for Common Subsequences Hypothesis • attr-size[n] set: level of node in lattice Metric Percent of attributescovered by common subsequences of URLs of various sizes

  9. Experiment: Applications Used • Bookstore web application • 9,748 LOC, 385 methods, 11 classes • Front end: JSP, Backend: MySql • 123 user sessions • uPortal application • 38,589 LOC, 4233 methods, 508 classes • Java, JSP, XML, J2EE • 2083 user sessions

  10. Results for Common Subsequences Hypothesis Bookstore

  11. Results for Common Subsequence Hypothesis Bookstore Result: subsequences of various sizes cover reasonable percent of attributes

  12. Conclusions for Common Subsequences Hypothesis • Between user sessions of a node there exists commonality in subsequences of URLs • These common subsequences cover a reasonable percent of URLs (attributes) of the node • Clustering based on single URLs • clusters similar use cases • can choose one object from each node

  13. Next-to-bottom Coverage of Use Cases Hypothesis In addition to covering all the URLs of the original test suite, the user sessions in next-to-bottom nodes execute a high percentage of the subsequences of URLs of the rest of the original test suite GD GR GL reduced set {us2, us6} remaining set {us1, us3, us4 us5} us1 us5 GS GB PL user sessions belonging to next-to-bottom nodes all other user sessions except sessions belonging to next-to-bottom nodes GM us4 us3 us2 us6

  14. Results for Next-to-bottom Coverage of Use Cases Hypothesis Metric: loss of coverage of use cases in remaining set by the reduced set Bookstore Result: short sequences present but long sequences are missing

  15. Conclusion for Next-to-bottom Coverage of Use Cases Hypothesis • Long sequences absent but smaller sequences are present in reduced set • reduced set contains more URLs hence may contain other URL sequences absent in remaining set • Moderately supports pickingnext-to-bottomnodes for reduced test suite

  16. Pros and Cons of Our Approach + Results from common subsequences hypothesis support using concept analysis for clustering user sessions + Experiments show little coverage loss (tech report) by reduced test suite • Results from next-to-bottom coverage of use cases hypothesis indicate further work needed on heuristic

  17. Future Work • Explore additional heuristics • Additional user session analysis • Useful for other software engineering tasks

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